Introduction to Mathematical Statistics |
From inside the book
Results 1-3 of 28
Page 158
... cent confidence interval for = EXERCISES = = - με H2 . 5.6 . Let the observed value of the mean X of a random sample of size 20 from a distribution which is n ( μ , 80 ) be 81.2 . Find a 95 per cent confidence interval for μ . - 5.7 ...
... cent confidence interval for = EXERCISES = = - με H2 . 5.6 . Let the observed value of the mean X of a random sample of size 20 from a distribution which is n ( μ , 80 ) be 81.2 . Find a 95 per cent confidence interval for μ . - 5.7 ...
Page 162
... cent confidence interval for 02/02 . EXERCISES 5.14 . If 8.6 , 7.9 , 8.3 , 6.4 , 8.4 , 9.8 , 7.2 , 7.8 , 7.5 are the observed values of a random sample of size 9 from a distribution that is n ( 8 , o2 ) , construct a 90 per cent ...
... cent confidence interval for 02/02 . EXERCISES 5.14 . If 8.6 , 7.9 , 8.3 , 6.4 , 8.4 , 9.8 , 7.2 , 7.8 , 7.5 are the observed values of a random sample of size 9 from a distribution that is n ( 8 , o2 ) , construct a 90 per cent ...
Page 183
... cent of the probability for the distribution of X is between y , and y ,. Let it be given that y = Pr [ F ( Y ) F ( Y1 ) ≥ p ] . Then the random interval ( Y1 , Y , ) has probability y of containing at least 100p per cent of the ...
... cent of the probability for the distribution of X is between y , and y ,. Let it be given that y = Pr [ F ( Y ) F ( Y1 ) ≥ p ] . Then the random interval ( Y1 , Y , ) has probability y of containing at least 100p per cent of the ...
Other editions - View all
Common terms and phrases
A₁ A₂ Accordingly best critical region c₁ cent confidence interval chi-square distribution complete sufficient statistic compute conditional p.d.f. confidence interval Consider continuous type critical region decision function defined degrees of freedom denote a random discrete type distribution function distribution having p.d.f. Equation Example EXERCISES function F(x given H₁ hypothesis H independent random variables integral joint p.d.f. k₁ Let X1 Let Y₁ limiting distribution marginal p.d.f. matrix maximum likelihood moment-generating function mutually stochastically independent noncentral normal distribution order statistics p.d.f. of Y₁ P(A₁ p₁ Poisson distribution positive integer probability density functions quadratic form random experiment random interval random sample random variables X1 respectively sample space Show significance level simple hypothesis statistic Y₁ stochastically independent random sufficient statistic theorem unbiased statistic variance o² w₁ X₁ X₁ and X2 X₂ Y₂ Z₁ zero elsewhere μ₁ μ₂ Σ Σ